Docs cleanup and Google-style tracker docstrings (#6751)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

docs/en/datasets/classify/imagewoof.md

@@ -60,6 +60,11 @@ ImageWoof dataset comes in three different sizes to accommodate various research
 To use these variants in your training, simply replace 'imagewoof' in the dataset argument with 'imagewoof320' or 'imagewoof160'. For example:
 
 ```python
+from ultralytics import YOLO
+
+# Load a model
+model = YOLO('yolov8n-cls.pt')  # load a pretrained model (recommended for training)
+
 # For medium-sized dataset
 model.train(data='imagewoof320', epochs=100, imgsz=224)
 

docs/en/datasets/detect/index.md

@@ -26,7 +26,7 @@ names:
   0: person
   1: bicycle
   2: car
-  ...
+  # ...
   77: teddy bear
   78: hair drier
   79: toothbrush

docs/en/datasets/pose/index.md

@@ -60,7 +60,7 @@ The `train` and `val` fields specify the paths to the directories containing the
 
 `names` is a dictionary of class names. The order of the names should match the order of the object class indices in the YOLO dataset files.
 
-(Optional) if the points are symmetric then need flip_idx, like left-right side of human or face. For example if we assume five keypoints of facial landmark: [left eye, right eye, nose, left mouth, right mouth], and the original index is [0, 1, 2, 3, 4], then flip_idx is [1, 0, 2, 4, 3] (just exchange the left-right index, i.e 0-1 and 3-4, and do not modify others like nose in this example).
+(Optional) if the points are symmetric then need flip_idx, like left-right side of human or face. For example if we assume five keypoints of facial landmark: [left eye, right eye, nose, left mouth, right mouth], and the original index is [0, 1, 2, 3, 4], then flip_idx is [1, 0, 2, 4, 3] (just exchange the left-right index, i.e. 0-1 and 3-4, and do not modify others like nose in this example).
 
 ## Usage
 

docs/en/datasets/segment/index.md

@@ -54,7 +54,7 @@ names:
   0: person
   1: bicycle
   2: car
-  ...
+  # ...
   77: teddy bear
   78: hair drier
   79: toothbrush